Closed schlichtanders closed 1 year ago
Hi @schlichtanders. There is some functionality implemented to add one observation at a time. Have a look at GPElastic
and see if that could solve your problem. If I remember correctly, this was developed specifically to support this use case in the BayesianOptimization
package. I should say this is for the exact case, not for MCMC with non-Gaussian likelihoods.
Thank you very much. Can GPElastic
also be used with the standard maximumlikelihood optimizer? Or is it only valid for the BayesianOptimization (e.g. following their intro example)
Another followup: Would this timeseries example with trend and cyclic information work with GPElastic? https://stor-i.github.io/GaussianProcesses.jl/latest/mauna_loa/
Yes, it should work with the standard maximum likelihood optimizer. But if I'm not mistaken, when you optimize the (covariance kernel and mean) hyperparameters, the algorithm will need to re-calculate the covariance matrix and its Cholesky decomposition at each step, so you will not see performance benefits from using GPElastic instead of GPE. You should expect to see a performance benefit when generating updated predictions with new observations while retaining the same hyperparameters.
Another followup: Would this timeseries example with trend and cyclic information work with GPElastic? https://stor-i.github.io/GaussianProcesses.jl/latest/mauna_loa/
I would expect it to work, yes. Let us know if it doesn't.
Hi, I am looking through probabilistic packages in Julia for modeling timeseries in a real-time such that my current model can be efficiently updated given a couple of new data points (maybe even one data point).
With Bayesian Modeling in general, I understood that I can put my previous state simply into the prior and practically use any Bayesian inference algorithm. I would like to understand whether I can do similar things with GaussianProcesses.jl
What I've found so far:
Would the BayesianOptimization the recommended approach for incremental ongoing learning?